The inference is ascertaining the probability of each potential cause given an observation [1]}. Approximate inference algorithms fall into two families: Monte Carlo algorithms and variational algorithms [2]}.
Note that, while computational neuroscientists often prefer to follow approximate inference, by exploring the biological implementation of Monte Carlo and variational methods,
inspired by [3]}, our approach here is based to model VHs through an adversarial inference setup.
Adversarial inference has some important advantages over standard Monte Carlo and variational approaches.
First, it can be applied to more complex models.
Second, the inference is more efficient than the standard Monte Carlo algorithms and it can use more flexible approximate posteriors compared to standard variational algorithms [3]}.
Moreover, GAN based adversarial learning techniques directly learn a generative model to construct high-quality data [5]}, and therefore usually more realistic than variational approaches.
